This series of files compile all analyses done during Chapter 1 for the regional assessment (Campaign 2016):
- Section 1 regroups maps, graphical ordinations and clustering.
- Section 2 presents SIMPER and regressions results.
All analyses have been done with PRIMER-e 6 and R 3.6.0.
Click on the table of contents in the left margin to assess a specific analysis
Click on a figure to zoom it
To assess Section 1, click here.
To go back to the summary page, click here.
Caracteristics of each campaign
| Sampling date |
|
August-September |
June to August |
July |
| Criteria for perturbation |
|
Potentially impacted if close to the city or industries, References outside the bay |
Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria |
Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria |
| Regions considered |
|
BSI |
BSI, CPC, BDA, MR |
BSI, MR |
| Number of sampled stations |
|
40 (20 HI, 20 R) |
78 (26 BSI, 19 CPC, 18 BDA, 15 MR) |
126 (111 BSI, 15 MR) |
| Parameters sampled |
Organic matter |
yes |
yes |
yes |
|
Photosynthetic pigments |
no |
yes |
yes |
|
Sediment grain-size |
yes |
yes |
yes |
|
Heavy-metals |
yes |
yes (for a limited number of stations) |
no (interpolated based on 2014 and 2016 values) |
| Benthic communities |
Compartment targeted |
Macro-infauna |
Macro-infauna |
Macro-infauna |
|
Sieved used |
500 µm |
1 mm |
500 µm and 1 mm |
|
Conservation technique |
Formaldehyle |
Formaldehyle |
Formaldehyle |
| Others |
|
N.A. |
N.A. |
N.A. |
We selected variables and characteristic species (using IndVal index and SIMPER procedure, see Section 2) for the analyses:
- Depth of the station: depth
- Percentage of organic matter: om
- Percentage of gravel: gravel
- Percentage of sand: sand
- Percentage of silt: silt
- Percentage of clay: clay
- Concentration of arsenic: arsenic
- Concentration of cadmium: cadmium
- Concentration of chromium: chromium
- Concentration of copper: copper
- Concentration of iron: iron
- Concentration of manganese: manganese
- Concentration of mercury: mercury
- Concentration of lead: lead
- Concentration of zinc: zinc
- Species richness: S
- Abundance of total individuals: N
- Shannon index: H
- Piélou evenness: J
- Abundance of Mesodesma arctatum: Marc
- Abundance of Cistenides granulata: Cgra
As there is missing data for metal concentrations outside BSI, two Designs have been used:
- Design 1: stations at BSI, CPC, BDA, MR with only habitat parameters
- Design 2: stations at BSI with habitat parameters and heavy metal concentrations.
Workspace preparation
Here, we use data from subtidal ecosystems (see metadata files for more information)
Only stations that have been sampled both for abiotic parameters and benthic species were included.
The script below includes personnal functions, refined data, parameters for each campaign and global means, sd, se.
1. Permutational Analyses of Variance
Results of univariate PermANOVAs on parameters and multivariate PermANOVA on the whole benthic community are presented in the table below.
| depth |
|
|
All regions in the same group |
| om |
|
S |
(CPC BDA MR) |
| gravel |
|
|
All regions in the same group |
| sand |
|
|
All regions in the same group |
| silt |
|
|
(BSI CPC BDA), (BDA MR) |
| clay |
|
|
(BSI BDA MR), (CPC MR) |
| S (1 mm) |
|
|
(BSI CPC MR), (CPC BDA MR) |
| N (1 mm) |
|
|
All regions in the same group |
| H (1 mm) |
|
S |
(CPC BDA MR), (BSI MR) |
| J (1 mm) |
|
|
(BSI CPC MR), (CPC BDA MR) |
| Marc (1 mm) |
|
|
(BSI BDA MR), (BSI CPC MR) |
| Cgra (1 mm) |
|
|
(BSI CPC BDA), (BSI BDA MR) |
| ALL SPECIES (1 mm) |
|
S |
|
2. IndVal and SIMPER
These analyses allowed to select species as dependant variables for the regressions. We used results from PRIMER to justify further their choice.
## cluster indicator_value probability
## cistenides_granulata 1 0.3080 0.018
## ennucula_tenuis 1 0.2222 0.006
## macoma_calcarea 1 0.2222 0.004
## eudorellopsis_integra 1 0.1556 0.038
## mesodesma_arctatum 2 0.2535 0.003
## harmothoe_imbricata 2 0.2008 0.005
## glycera_alba 2 0.1212 0.029
## psammonyx_nobilis 2 0.1212 0.036
##
## Sum of probabilities = 51.022
##
## Sum of Indicator Values = 6.08
##
## Sum of Significant Indicator Values = 1.6
##
## Number of Significant Indicators = 8
##
## Significant Indicator Distribution
##
## 1 2
## 4 4
SIMPER results (average dissimilarity: 94.67 )
| echinarachnius_parma |
0.131 |
0.205 |
0.638 |
4.18 |
1.33 |
0.138 |
| mesodesma_arctatum |
0.0987 |
0.205 |
0.482 |
3.52 |
0.267 |
0.242 |
| cistenides_granulata |
0.0671 |
0.128 |
0.525 |
0.485 |
1.62 |
0.313 |
| strongylocentrotus_sp |
0.0438 |
0.108 |
0.404 |
0.667 |
0.867 |
0.36 |
| nephtys_caeca |
0.034 |
0.0584 |
0.581 |
0.636 |
0.667 |
0.395 |
| protomedeia_grandimana |
0.0329 |
0.0819 |
0.402 |
0.636 |
0.933 |
0.43 |
| scoloplos_armiger |
0.0317 |
0.0798 |
0.397 |
1.06 |
1.11 |
0.464 |
| amphipholis_squamata |
0.0306 |
0.119 |
0.257 |
0.0606 |
2.96 |
0.496 |
| limecola_balthica |
0.0266 |
0.0543 |
0.49 |
0.606 |
0.489 |
0.524 |
| macoma_calcarea |
0.0241 |
0.0588 |
0.41 |
0 |
0.733 |
0.549 |
| thyasira_sp |
0.0233 |
0.0613 |
0.38 |
0.0303 |
0.933 |
0.574 |
| ennucula_tenuis |
0.0225 |
0.0545 |
0.413 |
0 |
1.02 |
0.598 |
| psammonyx_nobilis |
0.0203 |
0.0744 |
0.273 |
0.515 |
0 |
0.619 |
| harmothoe_imbricata |
0.0193 |
0.052 |
0.372 |
0.394 |
0.0222 |
0.64 |
| pygospio_elegans |
0.0193 |
0.0996 |
0.193 |
2.76 |
0.0222 |
0.66 |
| pontoporeia_femorata |
0.0151 |
0.0727 |
0.208 |
0 |
0.733 |
0.676 |
| glycera_alba |
0.0142 |
0.0566 |
0.25 |
0.515 |
0 |
0.691 |
| ameritella_agilis |
0.0141 |
0.0687 |
0.206 |
0 |
0.444 |
0.706 |
| astarte_undata |
0.014 |
0.0482 |
0.29 |
0.364 |
0.0222 |
0.721 |
| ciliatocardium_ciliatum |
0.0136 |
0.0497 |
0.273 |
0.182 |
0.222 |
0.735 |
| astarte_subaequilatera |
0.013 |
0.0485 |
0.268 |
0.364 |
0 |
0.749 |
| bipalponephtys_neotena |
0.0122 |
0.0715 |
0.17 |
0 |
0.556 |
0.762 |
| mya_arenaria |
0.0114 |
0.0264 |
0.433 |
0.0909 |
0.311 |
0.774 |
| goniada_maculata |
0.0109 |
0.0382 |
0.286 |
0.0303 |
0.333 |
0.785 |
| ampharete_oculata |
0.0104 |
0.0649 |
0.16 |
0.242 |
0 |
0.796 |
| nucula_proxima |
0.0096 |
0.0398 |
0.241 |
0 |
0.244 |
0.806 |
| glycera_dibranchiata |
0.00933 |
0.0351 |
0.266 |
0.0303 |
0.133 |
0.816 |
| diastylis_sculpta |
0.00903 |
0.0473 |
0.191 |
0.121 |
0.0444 |
0.826 |
| testudinalia_testudinalis |
0.00864 |
0.0382 |
0.226 |
0.212 |
0.0444 |
0.835 |
| ampharetidae_spp |
0.0069 |
0.0212 |
0.326 |
0.121 |
0.111 |
0.842 |
| eudorellopsis_integra |
0.00677 |
0.0217 |
0.312 |
0 |
0.333 |
0.849 |
| maldanidae_spp |
0.00645 |
0.0231 |
0.279 |
0.212 |
0.0444 |
0.856 |
| ampeliscidae_spp |
0.00641 |
0.0199 |
0.322 |
0.0909 |
0.0889 |
0.863 |
| yoldia_myalis |
0.00622 |
0.0215 |
0.289 |
0.0909 |
0.0667 |
0.87 |
| nephtys_ciliata |
0.00618 |
0.0252 |
0.245 |
0 |
0.2 |
0.876 |
| ophelia_limacina |
0.00592 |
0.0203 |
0.292 |
0.0606 |
0.0889 |
0.882 |
| phyllodoce_mucosa |
0.00572 |
0.0232 |
0.247 |
0 |
0.333 |
0.888 |
| polynoidae_spp |
0.00571 |
0.0156 |
0.366 |
0.0303 |
0.222 |
0.894 |
3. Univariate regressions
Independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices and characteristic species abundances.
i) Identification of outliers
To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.
We used linear models for the regressions on diversity indices, and generalized linear models with Poisson distribution for species abundances.
Design 1

Based on Cook’s Distance, we identified stations 74, 80, 87, 107, 111 and 126 as general outliers. They have been deleted for the following analyses of Design 1.
Design 2

Based on Cook’s Distance, we identified stations 104, 108 and 110 as general outliers. They have been deleted for the following analyses of Design 2.
ii) Correlations between parameters
Correlations have been calculated with Spearman’s rank coefficient.
Design 1
Correlation coefficients between habitat parameters (Design 1)
| depth |
1 |
0.498 |
0.112 |
-0.514 |
0.418 |
0.431 |
| om |
0.498 |
1 |
-0.075 |
-0.814 |
0.731 |
0.722 |
| gravel |
0.112 |
-0.075 |
1 |
-0.168 |
-0.385 |
-0.333 |
| sand |
-0.514 |
-0.814 |
-0.168 |
1 |
-0.781 |
-0.79 |
| silt |
0.418 |
0.731 |
-0.385 |
-0.781 |
1 |
0.97 |
| clay |
0.431 |
0.722 |
-0.333 |
-0.79 |
0.97 |
1 |

According to these results, the following variables are highly correlated so they have been considered together in the regressions (\(|\rho|\) > 0.80):
- om and sand (sand deleted)
- silt and clay (clay deleted)


Design 2
Correlation coefficients between habitat parameters and metals concentrations (Design 2)
| depth |
1 |
0.538 |
-0.309 |
-0.341 |
0.45 |
0.465 |
0.483 |
-0.069 |
0.3 |
0.424 |
0.332 |
-0.086 |
0.511 |
0.384 |
0.471 |
| om |
0.538 |
1 |
-0.606 |
-0.746 |
0.868 |
0.881 |
0.798 |
0.449 |
0.653 |
0.894 |
0.557 |
0.314 |
0.698 |
0.833 |
0.863 |
| gravel |
-0.309 |
-0.606 |
1 |
0.166 |
-0.699 |
-0.677 |
-0.374 |
-0.544 |
-0.344 |
-0.545 |
-0.308 |
-0.352 |
-0.134 |
-0.456 |
-0.596 |
| sand |
-0.341 |
-0.746 |
0.166 |
1 |
-0.786 |
-0.782 |
-0.636 |
-0.355 |
-0.684 |
-0.715 |
-0.527 |
-0.341 |
-0.554 |
-0.765 |
-0.699 |
| silt |
0.45 |
0.868 |
-0.699 |
-0.786 |
1 |
0.98 |
0.685 |
0.551 |
0.685 |
0.8 |
0.56 |
0.441 |
0.467 |
0.799 |
0.835 |
| clay |
0.465 |
0.881 |
-0.677 |
-0.782 |
0.98 |
1 |
0.704 |
0.512 |
0.702 |
0.828 |
0.555 |
0.401 |
0.506 |
0.814 |
0.856 |
| arsenic |
0.483 |
0.798 |
-0.374 |
-0.636 |
0.685 |
0.704 |
1 |
0.411 |
0.657 |
0.862 |
0.721 |
0.349 |
0.575 |
0.794 |
0.847 |
| cadmium |
-0.069 |
0.449 |
-0.544 |
-0.355 |
0.551 |
0.512 |
0.411 |
1 |
0.723 |
0.411 |
0.709 |
0.836 |
0.083 |
0.602 |
0.658 |
| chromium |
0.3 |
0.653 |
-0.344 |
-0.684 |
0.685 |
0.702 |
0.657 |
0.723 |
1 |
0.637 |
0.8 |
0.767 |
0.42 |
0.752 |
0.85 |
| copper |
0.424 |
0.894 |
-0.545 |
-0.715 |
0.8 |
0.828 |
0.862 |
0.411 |
0.637 |
1 |
0.546 |
0.286 |
0.51 |
0.832 |
0.861 |
| iron |
0.332 |
0.557 |
-0.308 |
-0.527 |
0.56 |
0.555 |
0.721 |
0.709 |
0.8 |
0.546 |
1 |
0.739 |
0.391 |
0.623 |
0.814 |
| manganese |
-0.086 |
0.314 |
-0.352 |
-0.341 |
0.441 |
0.401 |
0.349 |
0.836 |
0.767 |
0.286 |
0.739 |
1 |
0.09 |
0.499 |
0.578 |
| mercury |
0.511 |
0.698 |
-0.134 |
-0.554 |
0.467 |
0.506 |
0.575 |
0.083 |
0.42 |
0.51 |
0.391 |
0.09 |
1 |
0.559 |
0.492 |
| lead |
0.384 |
0.833 |
-0.456 |
-0.765 |
0.799 |
0.814 |
0.794 |
0.602 |
0.752 |
0.832 |
0.623 |
0.499 |
0.559 |
1 |
0.821 |
| zinc |
0.471 |
0.863 |
-0.596 |
-0.699 |
0.835 |
0.856 |
0.847 |
0.658 |
0.85 |
0.861 |
0.814 |
0.578 |
0.492 |
0.821 |
1 |

According to these results, the following variables are highly correlated so they have been considered together in the regressions (\(|\rho|\) > 0.80):
- om and copper (copper deleted)
- silt and clay (clay deleted)
- lead and zinc (zinc deleted)
We also decided to exclude sand content in the regressions, as it tends to increase drasticaly VIFs due to a marginal negative correlation with silt and clay.



iii) Simple regressions
These analyses have been do to explore the relationships between variables. As it is a huge number of results to interpret, only multiple regressions will be included in the article (see below).
Design 1
Adjusted R-squared of simple regressions for Design 1
| S |
0.1745 |
0.08189 |
0.01292 |
0.1223 |
| N |
-0.005956 |
0.01079 |
0.002965 |
0.02872 |
| H |
0.2179 |
0.08459 |
-0.01115 |
0.1075 |
| J |
0.02881 |
0.001654 |
0.006298 |
0.01015 |
| Marc |
0.02454 |
-0.01282 |
-0.007057 |
-0.008682 |
| Cgra |
0.00757 |
-0.0121 |
-0.01174 |
-0.01398 |
p-values of simple regressions for Design 1
| S |
0.0001546 |
0.008504 |
0.1692 |
0.001523 |
| N |
0.449 |
0.1872 |
0.2749 |
0.0827 |
| H |
2.13e-05 |
0.007585 |
0.6429 |
0.002865 |
| J |
0.08235 |
0.2941 |
0.2326 |
0.1929 |
| Marc |
0.09955 |
0.7512 |
0.4808 |
0.5349 |
| Cgra |
0.2185 |
0.6983 |
0.676 |
0.8853 |
Design 2
Adjusted R-squared of simple regressions for Design 2
| S |
0.2707 |
0.03698 |
0.1377 |
0.1268 |
-0.02305 |
-0.04725 |
-0.02327 |
-0.04316 |
-0.04679 |
-0.04713 |
0.102 |
| N |
0.09012 |
0.06662 |
0.148 |
0.1598 |
0.009787 |
-0.02218 |
0.009648 |
-0.02519 |
-0.04584 |
-0.04743 |
0.1177 |
| H |
0.3677 |
0.0403 |
0.1874 |
0.1195 |
-0.03075 |
-0.04719 |
-0.02167 |
-0.03706 |
-0.04762 |
-0.02843 |
0.08102 |
| J |
0.0211 |
-0.04663 |
0.002418 |
-0.04554 |
-0.04376 |
-0.03974 |
-0.04241 |
-0.04181 |
-0.03541 |
-0.03347 |
-0.04581 |
| Marc |
-0.01444 |
-0.02484 |
-0.0277 |
-0.001265 |
-0.01935 |
0.08445 |
0.0811 |
0.04514 |
-0.00923 |
-0.01731 |
0.06695 |
| Cgra |
-0.04616 |
-0.04259 |
-0.03938 |
-0.04446 |
-0.04717 |
0.03627 |
0.02581 |
-0.01584 |
0.01732 |
-0.0205 |
-0.02944 |
p-values of simple regressions for Design 2
| S |
0.006407 |
0.1888 |
0.04566 |
0.05328 |
0.4854 |
0.9324 |
0.4874 |
0.7675 |
0.8984 |
0.9222 |
0.07545 |
| N |
0.08905 |
0.1238 |
0.03946 |
0.03336 |
0.2823 |
0.4777 |
0.2829 |
0.5053 |
0.8519 |
0.9519 |
0.06052 |
| H |
0.001285 |
0.18 |
0.02243 |
0.05903 |
0.564 |
0.927 |
0.4733 |
0.6485 |
0.9941 |
0.5381 |
0.1012 |
| J |
0.2382 |
0.8892 |
0.3164 |
0.8399 |
0.7832 |
0.694 |
0.7491 |
0.7355 |
0.6239 |
0.5975 |
0.8506 |
| Marc |
0.4166 |
0.5019 |
0.5304 |
0.3354 |
0.4539 |
0.09641 |
0.101 |
0.1679 |
0.3816 |
0.4378 |
0.1233 |
| Cgra |
0.8656 |
0.7535 |
0.6875 |
0.8033 |
0.9253 |
0.1908 |
0.2222 |
0.4267 |
0.252 |
0.4633 |
0.5491 |
iv) Multiple regressions
This section presents analyses done (i) to determine which model (Design 1, Design 2 metals, parameters or all) decribes the best the parameters and (ii) which variables are the most important to explain the parameters.
We used linear models for the regressions on diversity indices, and generalized linear models with Poisson distribution for species abundances. We also used ZIP models, but they are “computationally” singular, so they have not been computed here.
A. Best model selection
The aim is to descriminate the effect of habitat parameters and heavy metal concentrations on the dependant variables.
Results of the model selection are summurized below (according to AIC).
| Design 1 |
341.7 |
679.1 |
111.4 |
17.11 |
538.9 |
325.6 |
| Design 2 |
116 |
211.8 |
33.57 |
4.742 |
30.93 |
86.1 |
| Metals Design 2 |
119.8 |
208.6 |
41.85 |
7.091 |
22.93 |
84.73 |
| Parameters Design 2 |
114.9 |
202.7 |
27.2 |
1.246 |
40.16 |
118.3 |
Species richness
| Parameters |
23 |
6 |
114.9 |
0 |
0.34 |
| Design 2 |
23 |
13 |
116 |
1.112 |
0.38 |
| Metals |
23 |
9 |
119.8 |
4.979 |
0.24 |
| Design 1 |
72 |
6 |
341.7 |
226.8 |
0.23 |
Total abundance
| Parameters |
23 |
6 |
202.7 |
0 |
0.2 |
| Metals |
23 |
9 |
208.6 |
5.925 |
0.04 |
| Design 2 |
23 |
13 |
211.8 |
9.182 |
-0.07 |
| Design 1 |
72 |
6 |
679.1 |
476.5 |
0 |
Shannon index
| Parameters |
23 |
6 |
27.2 |
0 |
0.46 |
| Design 2 |
23 |
13 |
33.57 |
6.374 |
0.37 |
| Metals |
23 |
9 |
41.85 |
14.65 |
0.06 |
| Design 1 |
72 |
6 |
111.4 |
84.21 |
0.23 |
Piélou’s evenness
| Parameters |
23 |
6 |
1.246 |
0 |
-0.09 |
| Design 2 |
23 |
13 |
4.742 |
3.496 |
-0.13 |
| Metals |
23 |
9 |
7.091 |
5.845 |
-0.3 |
| Design 1 |
72 |
6 |
17.11 |
15.86 |
0.02 |
Abundance of M. arctatum
| Metals |
23 |
8 |
22.93 |
0 |
0.89 |
| Design 2 |
23 |
12 |
30.93 |
8 |
0.89 |
| Parameters |
23 |
5 |
40.16 |
17.23 |
0.52 |
| Design 1 |
72 |
5 |
538.9 |
515.9 |
0.15 |
Abundance of C. granulata
| Metals |
23 |
8 |
84.73 |
0 |
0.38 |
| Design 2 |
23 |
12 |
86.1 |
1.376 |
0.44 |
| Parameters |
23 |
5 |
118.3 |
33.6 |
0.03 |
| Design 1 |
72 |
5 |
325.6 |
240.9 |
0.04 |
B. Significative variables selection
We identified which variables are selected after an AIC procedure to best predict the variation of the parameters.
Design 1
Results of the variables selection are summurized below (according to AIC).
| depth |
+ |
|
+ |
+ |
- |
+ |
| om |
|
|
+ |
|
+ |
- |
| gravel |
- |
|
|
|
- |
- |
| silt |
+ |
+ |
|
|
- |
|
| Adjusted-R2 |
0.23 |
0.03 |
0.24 |
0.03 |
|
|
| McFadden Pseudo-R2 |
|
|
|
|
0.15 |
0.04 |
Abundance of M. arctatum
## FULL MODEL (Poisson)
## McFadden's Pseudo-R2 is: 0.15
Fitting generalized (poisson/log) linear model: Marc ~ depth + om + gravel + silt
| (Intercept) |
1.425 |
0.1385 |
10.29 |
7.436e-25 |
* * * |
| depth |
-0.05847 |
0.01034 |
-5.657 |
1.544e-08 |
* * * |
| om |
0.6713 |
0.2503 |
2.682 |
0.007325 |
* * |
| gravel |
-6.993 |
2.532 |
-2.762 |
0.005739 |
* * |
| silt |
-1.83 |
0.788 |
-2.323 |
0.02018 |
* |
Variance Inflation Factors
| VIF |
1.12 |
2.3 |
1.02 |
2.3 |
## REDUCED MODEL (Poisson)
## McFadden's Pseudo-R2 is: 0.15
Fitting generalized (poisson/log) linear model: Marc ~ depth + om + gravel + silt
| (Intercept) |
1.425 |
0.1385 |
10.29 |
7.436e-25 |
* * * |
| depth |
-0.05847 |
0.01034 |
-5.657 |
1.544e-08 |
* * * |
| om |
0.6713 |
0.2503 |
2.682 |
0.007325 |
* * |
| gravel |
-6.993 |
2.532 |
-2.762 |
0.005739 |
* * |
| silt |
-1.83 |
0.788 |
-2.323 |
0.02018 |
* |
Variance Inflation Factors
| VIF |
1.12 |
2.3 |
1.02 |
2.3 |
## Analysis of Deviance Table
##
## Model 1: Marc ~ depth + om + gravel + silt
## Model 2: Marc ~ depth + om + gravel + silt
## Resid. Df Resid. Dev Df Deviance
## 1 67 484.95
## 2 67 484.95 0 0
## FULL MODEL (Zero-Inflated Poisson)
Fitting corresponding ZIP generalized linear model (counts)
| (Intercept) |
3.508 |
0.375 |
9.355 |
8.326e-21 |
| depth |
0.01816 |
0.02379 |
0.7634 |
0.4452 |
| om |
-4.617 |
1.827 |
-2.527 |
0.01151 |
| gravel |
-11.6 |
3.815 |
-3.04 |
0.002365 |
| silt |
13.81 |
4.593 |
3.007 |
0.002635 |
Fitting corresponding ZIP generalized linear model (zeros)
| (Intercept) |
1.389 |
0.7358 |
1.887 |
0.05911 |
| depth |
0.03819 |
0.03606 |
1.059 |
0.2896 |
| om |
-2.226 |
2.221 |
-1.002 |
0.3161 |
| gravel |
-7.292 |
7.558 |
-0.9648 |
0.3347 |
| silt |
7.604 |
6.283 |
1.21 |
0.2262 |
## REDUCED MODEL
Fitting corresponding ZIP generalized linear model (counts)
| (Intercept) |
3.389 |
0.3237 |
10.47 |
1.183e-25 |
| om |
-3.691 |
1.205 |
-3.063 |
0.00219 |
| gravel |
-9.076 |
2.491 |
-3.644 |
0.0002687 |
| silt |
11.69 |
3.256 |
3.591 |
0.0003292 |
Fitting corresponding ZIP generalized linear model (zeros)
| (Intercept) |
1.681 |
0.6162 |
2.728 |
0.006375 |
| om |
-1.838 |
1.848 |
-0.9949 |
0.3198 |
| gravel |
-2.703 |
5.371 |
-0.5032 |
0.6148 |
| silt |
7.965 |
5.849 |
1.362 |
0.1733 |

Abundance of C. granulata
## FULL MODEL (Poisson)
## McFadden's Pseudo-R2 is: 0.04
Fitting generalized (poisson/log) linear model: Cgra ~ depth + om + gravel + silt
| (Intercept) |
0.002788 |
0.202 |
0.0138 |
0.989 |
|
| depth |
0.02176 |
0.006089 |
3.574 |
0.0003513 |
* * * |
| om |
-0.3445 |
0.3182 |
-1.083 |
0.279 |
|
| gravel |
-1.903 |
1.42 |
-1.34 |
0.1801 |
|
| silt |
-0.2409 |
0.6917 |
-0.3483 |
0.7276 |
|
Variance Inflation Factors
| VIF |
1.13 |
1.61 |
1.03 |
1.66 |
## REDUCED MODEL (Poisson)
## McFadden's Pseudo-R2 is: 0.04
Fitting generalized (poisson/log) linear model: Cgra ~ depth + om + gravel
| (Intercept) |
0.01584 |
0.1988 |
0.07969 |
0.9365 |
|
| depth |
0.02131 |
0.005967 |
3.571 |
0.000355 |
* * * |
| om |
-0.4251 |
0.2243 |
-1.895 |
0.0581 |
|
| gravel |
-1.795 |
1.39 |
-1.292 |
0.1965 |
|
Variance Inflation Factors
| VIF |
1.11 |
1.12 |
1.01 |
## Analysis of Deviance Table
##
## Model 1: Cgra ~ depth + om + gravel + silt
## Model 2: Cgra ~ depth + om + gravel
## Resid. Df Resid. Dev Df Deviance
## 1 67 253.86
## 2 68 253.98 -1 -0.12355
## FULL MODEL (Zero-Inflated Poisson)
Fitting corresponding ZIP generalized linear model (counts)
| (Intercept) |
2.315 |
0.2812 |
8.233 |
1.819e-16 |
| depth |
-0.02214 |
0.009449 |
-2.343 |
0.01913 |
| om |
-0.0003962 |
0.3687 |
-0.001075 |
0.9991 |
| gravel |
-2.308 |
2.178 |
-1.06 |
0.2891 |
| silt |
-0.9407 |
0.9178 |
-1.025 |
0.3054 |
Fitting corresponding ZIP generalized linear model (zeros)
| (Intercept) |
2.109 |
0.6416 |
3.287 |
0.001014 |
| depth |
-0.06589 |
0.03013 |
-2.187 |
0.02876 |
| om |
0.4441 |
0.7106 |
0.625 |
0.532 |
| gravel |
1.887 |
4.009 |
0.4707 |
0.6379 |
| silt |
-0.7454 |
1.873 |
-0.398 |
0.6906 |
## REDUCED MODEL
Fitting corresponding ZIP generalized linear model (counts)
| (Intercept) |
2.089 |
0.2421 |
8.631 |
6.107e-18 |
| depth |
-0.02287 |
0.008713 |
-2.625 |
0.008665 |
Fitting corresponding ZIP generalized linear model (zeros)
| (Intercept) |
2.172 |
0.5507 |
3.944 |
8e-05 |
| depth |
-0.05481 |
0.0213 |
-2.574 |
0.01007 |


Design 2
Results of the variables selection are summurized below (according to AIC).
| depth |
+ |
|
+ |
+ |
NA |
NA |
| om |
- |
|
- |
|
NA |
NA |
| gravel |
- |
- |
- |
|
NA |
NA |
| silt |
|
|
|
+ |
NA |
NA |
| arsenic |
|
|
|
|
NA |
NA |
| cadmium |
- |
|
- |
|
NA |
NA |
| chromium |
- |
|
|
|
NA |
NA |
| iron |
- |
- |
- |
|
NA |
NA |
| manganese |
+ |
|
+ |
+ |
NA |
NA |
| mercury |
- |
- |
|
|
NA |
NA |
| lead |
+ |
+ |
+ |
- |
NA |
NA |
| Adjusted-R2 |
0.47 |
0.27 |
0.48 |
0.17 |
|
|
| McFadden Pseudo-R2 |
|
|
|
|
NA |
NA |
Abundance of M. arctatum
Abundances are extremely low, making VIFs very high. This regression is not possible to interpret.
Abundance of C. granulata
Abundances are extremely low, making VIFs very high. This regression is not possible to interpret.
4. Multivariate regressions
Independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices and characteristic species abundances.
Sand variables (Csand, Msand, Fsand) and mud variables (silt, clay) were merged to reduced the problem of model overfitting.
This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.
For Design 1:
For Design 2: